In this section, we use experiments to verify the effectiveness of AMSGrad-EG and AMSGrad-EG-DRD algorithms by applying Wasserstein GAN [1]} on the MNIST [2]} and Fashion MNIST [3]} datasets in our experiments. More experiments will be shown in the appendix. The architectures of discriminator and generator are set to be MLP. The layer widths of generator MLP are 100, 128, 784 (figure's dimension) and the layer widths of discriminator MLP are 784, 128, 1. We set batch sizes as 64, learning rate as \(1e\) -4 and we compare AMSGrad-EG, AMSGrad-EG-DRD and SGDA by drawing their generated figures after 10k, 20k, 50k iterations in the following Figures REF  and REF . We use the Tensorflow framework [4]} to complete our experiments. As a result, unlike the non-adaptive SGDA algorithm, the two algorithms proposed by us perform better than the non-adaptive SGDA and their generated figures are almost real, which shows their effectiveness.
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